OBJECTIVE: This study aims to evaluate the association between indoor airborne fungi and environmental factors in a student dormitory in southern Thailand.
MATERIAL AND METHODS: The study was conducted at Walailak University in southern Thailand from September toDecember 2020. Air samples were collected from rooms in thirteen dormitories, and the fungal load was determined using the passive air sampling method. The study also measured meteorological parameters and gathered data on occupant behaviors and exposure-related symptoms through a self-administered questionnaire.
RESULTS: In a total of 135 student rooms, the average concentration (mean ± SD) of indoor airborne fungi was 409.72±176.22 CFU/m3, which showed the highest concentration on the first floor. For meteorological parameters, the averages of RH (%), temperature (°C), and CO2 (ppm) were 70.99±2.37, 31.11±0.56 and 413.29±76.72, respectively. The abundance of indoor airborne fungi was positively associated with an increase in RH (β=0.267, 95% CI: 5.288, 34.401) and building height (β=0.269, 95% CI: 16.283, 105.873), with values of 19.845 and 61.078, respectively. Conversely, temperature exhibited a negative effect on indoor airborne fungi (-92.224, β=-0.292, 95% CI: -150.052, -34.396).
CONCLUSION: The findings highlight the influence of RH, temperature and building height on indoor airborne fungi in the student dormitory. Therefore, effective management strategies are necessary to improve indoor air quality and reduce associated health risks in student dormitories.
METHODS: A single case report of a female patient with the diagnosis of REAH, detailing her presenting symptoms, clinical findings, management and follow up.
RESULTS: Histopathological assessment of the excised nasopharyngeal polyp was consistent with a diagnosis of REAH with a discussion on the disease and its current literature reviews.
CONCLUSION: The incidence of REAH within the nasopharynx remain rare with only few cases described in literature, especially in females.
OBJECTIVE: The objective of this systematic review was to analyze the various studies involving photobiomodulation therapy on neuropathic pain and plantar pressure distribution in diabetic peripheral neuropathy.
METHODS: We conducted a systematic review (PubMed, Web of Science, CINAHL, and Cochrane) to summarise the evidence on photobiomodulation therapy for Diabetic Peripheral Neuropathy with type 2 diabetes mellitus. Randomized and non-randomized studies were included in the review.
RESULTS: This systematic review included eight studies in which photobiomodulation therapy showed improvement in neuropathic pain and nerve conduction velocity. It also reduces plantar pressure distribution, which is a high risk for developing foot ulcers.
CONCLUSION: We conclude that photobiomodulation therapy is an effective, non-invasive, and costefficient means to improve neuropathic pain and altered plantar pressure distribution in diabetic peripheral neuropathy.
OBJECTIVE: The objective of this study was to evaluate whether SOS exerts fungicidal activities against common fungal species.
MATERIALS AND METHODS: The efficacy of SOS was tested against 6 fungal species (Candida albicans, Candida auris, Candida tropicalis, Candida parapsilosis, Sporothrix schenckii, Trichophyton mentagrophytes) using an in vitro time-kill assay.
RESULTS: SOS achieved 99.9999% reduction of all tested fungi within 1 minute of exposure.
CONCLUSIONS: This study shows that SOS may be an effective tool for the prevention and control of fungal infections.
METHODS: A systematic literature search was performed on five electronic databases from database inception to 3 November 2021. A two-step technique was used in the data synthesis process: (i) the barriers of LTBI management were identified using the COM-B model, followed by (ii) mapping of intervention functions from BCW to address the identified barriers.
RESULTS: Forty-seven eligible articles were included in this review. The findings highlighted the need for a multifaceted approach in tackling the barriers in LTBI management across the public, provider and system levels. The barriers were summarized into suboptimal knowledge and misperception of LTBI, as well as stigma and psychosocial burden, which could be overcome with a combination of intervention functions, targeting education, environment restructuring, persuasion, modelling, training, incentivization and enablement.
CONCLUSIONS: The remedial strategies using BCW to facilitate policy reforms in LTBI management could serve as a value-added initiative in the global tuberculosis control and prevention program.
METHODS: We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance.
RESULTS: We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent.
CONCLUSIONS: Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.